Global Navigation Satellite System (GNSS) receivers are electronic devices that receive and digitally process signals transmitted by satellites to, for example, estimate time and position data. Stationary receivers are primarily used to obtain precise time while mobile receivers often also calculate geospatial location of the receiver. Data produced by these receivers is often of critical importance in business operations, logistics, navigation, robotics, and other applications.
However, signals from GNSS, such as from the United States Global Positioning System (GPS), may be weak and can be blocked or compromised accidently or on purpose. For example, adding a new GPS antenna to a rooftop over a data center could produce interference that would partially or even completely interfere with operation of other antennas. Injection of false information into the satellite signal is also a danger. Researchers have demonstrated that it is possible to receive and then retransmit modified GPS signals in order to change the time or position information on a GPS receiver.
GNSS satellites may transmit messages encoding time and orbital information that receivers may digitally receive and process in order to calculate receiver position and precise time. In view of the fragility of these receivers and the critical nature of their operation, some methods have been proposed to monitor and detect problems.
Heat maps and azimuth or elevation maps may be used to depict which satellites are visible at a given instance in time using “current state” data. For example, FIG. 1 depicts an azimuth/elevation chart of GPS satellite positions.
FIG. 1 is a typical flat satellite azimuth/elevation map. It gives “current state” data about which satellites are currently visible or have been visible over some period of data collection. An azimuth/elevation map may depict the four cardinal directions (e.g., North “N,” South “S,” East “E,” West “W”) with circles at different coordinates in the map that indicate the positions of one or more satellites in a visible sky. However, the current state information in the map of FIG. 1 may be only of limited use because it does not show any information about satellite obstructions, only the current state of visible satellites.
FIG. 2 depicts a heat map for visualizing satellite signal strength. A heat map may refer generally to any graphical representation of data where the individual values contained in a matrix may be represented as colors or by grayscale. In FIG. 2, the regions marked by lines spaced farther apart may indicate, for example, a higher satellite signal strength, whereas the regions marked by lines spaced closer together or cross hatched may indicate, for example, a lower satellite signal strength. FIG. 2 depicts a heat map for visualizing signal strength that is overlaid over a geophysical map. Various techniques for collecting current state signal strength data and building a heat-map correlated to a geophysical map are known to those skilled in the art, as shown in FIG. 2.
FIG. 2 is from an existing open source radio project that takes data that is output from a Register-transfer level chipset (e.g., RTL2832U) Software Defined Radio (RTL-SDR) Scanner (e.g., a program that collects signal strength data over any desired bandwidth) and at the same time records GPS coordinates using an external GPS receiver. See “Creating a Signal Strength Heatmap with an RTL-SDR,” available at http://www.rtl-sdr.com/creating-signal-strength-heatmap-rtl-sdr/ (Sep. 25, 2014), and “GSM heatmap using RTL-SDR” comments, available at http://www.reddit.com/r/RTLSDR/comments/2hbjyt/gsm_heatmap_using_rtlsdr/ (Sep. 24, 2014). Additionally, a paper in Science Reports, Xingxing Li et al., “Precise positioning with current multi-constellation Global Navigation Satellite Systems: GPS, GLONASS, Galileo and BeiDou Scientific Reports 5, Article number: 8328 doi:10.1038/srep08328 (Feb. 9, 2015), available at http://www.nature.com/srep/2015/150209/srep08328/full/srep08328.html (“Li”), which is hereby incorporated herein by reference in its entirety, describes some standard data collection and analysis methods applied to GPS systems.
FIG. 3 depicts a time series of GPS satellite positions, taken from Li. FIGS. 4A and 4B depict a signal-to-noise ratio of different satellite systems and orbital types at the GMSD station (Japan), taken from Li. FIGS. 3 and 4A-B depict both the satellite tracks of visible satellites and the signal strength information being collected over time. However, since each point may correspond to signals from multiple satellites with configurations changing over time, there is no straightforward or optimal way to see whether there are obstructions from this data or to detect spoofing that could change signal strength over some spatial region.